Florence AI Navigator

Comprehensive Sales Manual
Version 3.0 — February 2026
NightingaleMD Sales Enablement
Confidential & Proprietary. This document is intended solely for NightingaleMD sales representatives and authorized personnel. All patient data shown in screenshots is simulated demo data and does not represent actual patients. All statistics cited herein are based on internal pilot program data and are subject to change. This product is not a medical device and does not provide clinical diagnoses.

Table of Contents

PartChapterTopic
Part 1
Strategic Context
Chapter 1Introduction & Value Proposition
Chapter 2The Traditional CCA Process (Before Florence)
Chapter 3The CCA Lifecycle with Florence (5 Phases)
Part 2
Pre-Encounter
Chapter 4The Navigator's View — Automating Pre-Encounter Tasks
Chapter 5The Engage Workflow — AI-Powered Patient Outreach
Part 3
Encounter
Chapter 6The Provider's View — A Seamless Encounter
Chapter 7Provider Best Practices & Objection Handling
Part 4
Post-Encounter
Chapter 8The Convene Workflow — Care Team Collaboration
Chapter 9The Check-In Workflow — Proactive Patient Monitoring
Chapter 10Filing to the EHR & Conclusion
AppendicesA & BScreenshot Reference & Demo Cheat Sheet

Part 1: Strategic Context & Value Proposition

Chapter 1: Introduction

Audience: This manual is designed for sales representatives at NightingaleMD. It provides the strategic context, workflow knowledge, and demo scripts needed to effectively showcase the Florence AI Navigator to prospective clients, including clinical stakeholders (CMOs, quality directors) and practice managers.

Purpose: The primary purpose of this manual is to serve as a comprehensive guide for conducting live client demos. It will equip you with the knowledge to not only demonstrate the features of Florence but also to articulate its profound value proposition within the complex ecosystem of Comprehensive Care Assessment (CCA).

Key Performance Indicators

70% Navigator Workload Reduction Based on internal pilot data, Q3 2025*
40% Gap Closure Rate Improvement Based on internal pilot data, Q3 2025*
100% Automated Documentation MEAT-criteria compliant notes*

*These metrics are based on NightingaleMD's internal pilot program conducted in Q3 2025 with a sample of 12 participating practices. Results may vary based on practice size, patient population, and implementation approach. Full methodology available upon request.

MetricImpactDescription
Navigator Workload Reduction70%*Automates manual tasks like chart review, patient outreach, and documentation preparation, freeing navigators to focus on high-value patient interactions.
Gap Closure Rate Improvement40%*Proactively identifies and stages all care gaps from COOP, ensuring providers have the information they need at the point of care.
Automated Documentation100%*Generates compliant, MEAT-criteria documentation in real-time as providers accept gaps, eliminating the documentation burden.
Enhanced Patient EngagementSignificantUtilizes AI-powered voice and SMS to conduct TCM outreach, schedule appointments, and monitor patients.

Chapter 2: The Traditional CCA Process (Before Florence)

The traditional Comprehensive Care Assessment (CCA) process is a manual, time-consuming, and often inefficient workflow that places a heavy burden on care navigators and providers. Understanding these pain points is critical to articulating the value of Florence.

The Manual CCA Workflow

  1. Manual Data Review: The care navigator manually reviews data from the Care Opportunities and Outcomes Platform (COOP) to identify patients due for a CCA (e.g., recently discharged, due for an AWV).
  2. Manual Chart Review: The navigator manually reviews the patient's chart in the EHR to identify all potential care gaps, including suspect diagnoses, quality measures, and frailty indicators.
  3. Manual Patient Outreach: The navigator manually calls the patient to schedule a Transitional Care Management (TCM) call and a follow-up appointment.
  4. Manual Documentation Preparation: Before the appointment, the navigator manually prepares all necessary documentation, including a summary of care gaps and supporting evidence.
  5. Provider Encounter: During the visit, the provider must manually review the patient's chart and the navigator's notes to address care gaps, often without real-time decision support.
  6. Manual Follow-up: After the visit, the navigator manually coordinates any necessary follow-up, such as specialist referrals or patient education.

Key Pain Points

Pain PointDescription
Time-ConsumingThe manual process can take 4–6 hours of navigator time per patient, limiting the number of patients a single navigator can manage.
Inconsistent Gap IdentificationManual chart review is prone to human error, leading to missed care gaps and lost revenue opportunities.
Provider Documentation BurdenProviders spend significant time on documentation, taking away from patient care.
Missed Follow-upManual follow-up is often inconsistent, leading to poor care coordination and patient outcomes.
Limited ScalabilityThe manual process is not scalable, making it difficult for practices to manage a growing patient population.

Chapter 3: The CCA Lifecycle with Florence

Florence transforms the CCA process by automating and enhancing each phase of the lifecycle, from trigger to ongoing management. This integrated approach ensures a seamless, efficient, and effective workflow across all five phases.

Phase 1: Trigger

Florence automatically detects CCA triggers from COOP in real-time, including hospital discharge notifications, Annual Wellness Visit (AWV) due dates, new quality measure gaps, and suspect diagnosis gaps. When a trigger is detected, Florence immediately begins compiling the patient's clinical profile and initiating the pre-encounter workflow. In the demo, Jane Doe's discharge from St. Joseph's Hospital triggered Florence to begin the entire CCA process automatically.

Phase 2: Pre-Encounter (Engage)

Once a trigger is detected, Florence initiates the pre-encounter workflow. This includes compiling all necessary CCA components from COOP and the EHR, automatically scheduling and conducting the TCM outreach call using the Engage workflow, confirming the follow-up appointment with the patient, and staging all identified care gaps for provider review. The Engage workflow uses AI-powered voice to conduct natural, empathetic conversations with patients, handling medication verification, appointment scheduling, and symptom screening.

Phase 3: Encounter

During the patient visit, Florence acts as a real-time copilot for the provider. The Nightingale Navigator sidebar displays all staged gaps with supporting evidence directly within athenaOne. The provider can accept, reject, or defer each gap with a single click, and Florence auto-generates MEAT-criteria documentation in real-time as gaps are accepted. This eliminates the documentation burden and ensures compliance.

Phase 4: Post-Encounter (Convene)

After the visit, Florence automates the post-encounter workflow. This includes coordinating specialist referrals and follow-up appointments, sending automated patient education materials, and using the Convene workflow to facilitate care team meetings. Convene enables three-way calls between the patient, provider, and specialists, with real-time transcription and documentation. The File to EHR function uses generative browsing to securely file all documentation to athenaOne with a single click.

Phase 5: Ongoing Chronic Condition Management (Check-In)

Florence provides ongoing support for patients with chronic conditions using the Check-In workflow. This SMS-based monitoring system conducts routine check-ins, tracks medication adherence and care plan progress, monitors symptoms, and escalates to a human navigator when necessary. For Jane Doe, this means daily post-discharge monitoring to prevent readmission and ensure recovery compliance.

Key Terminology — COOP: The Care Opportunities and Outcomes Platform (COOP) is the external data source that provides gap validation, suspect diagnosis identification, and quality measure tracking. Florence integrates with COOP to ensure all care gaps are accurate and up-to-date. The COOP badge visible on care gaps in the sidebar indicates that the gap has been validated by the COOP platform.

Part 2: The Pre-Encounter Workflow & Demo Scripts

Chapter 4: The Navigator's View — Automating Pre-Encounter Tasks

This chapter details the pre-encounter workflow from the care navigator's perspective. The goal is to demonstrate how Florence automates the most time-consuming manual tasks, allowing navigators to operate at the top of their license.

Demo Scenario: Patient Jane Doe, 68 y/o female, was recently discharged from St. Joseph's Hospital after an acute MI. Florence received a real-time discharge notification from COOP and has already compiled her clinical profile, identified 9 care gaps, and prepared for the TCM outreach call.
1
Open the Florence Dashboard

When you first open the Florence AI Navigator, you see the side-by-side view: the athenaOne EHR on the left and the Florence Copilot sidebar on the right. The patient's chart is already loaded with all relevant clinical data, including the HPI, vitals, screenings, problem list, and medications.

Florence Dashboard Overview
Figure 1: The Florence AI Navigator integrated into athenaOne. The left panel displays the patient's EHR chart (Subjective, Objective, Problem List), while the right panel shows the Nightingale Navigator sidebar with patient summary, workflow buttons (Engage, Convene, Check-In), and Care & Diagnosis Gaps.
2
Review the Patient Summary

The Florence Copilot sidebar immediately shows the patient's key information at a glance. Jane Doe is flagged as High Risk and enrolled in CCM (Chronic Care Management). The three workflow buttons — Engage, Convene, and Check-In — are prominently displayed. Below the buttons, the Florence Summary dropdown provides a quick clinical overview, and the Care & Diagnosis Gaps section shows all 9 identified gaps.

Florence Copilot Sidebar
Figure 2: The Florence Copilot sidebar showing patient summary (Jane Doe, 68 y/o F, High Risk, CCM, MRN: 202070), Sync and Post-Visit action buttons, workflow buttons (Engage, Convene, Check-In), Florence Summary dropdown, and the first two of 9 Care & Diagnosis Gaps with category filters.
3
Review Care & Diagnosis Gaps

The Care & Diagnosis Gaps section shows all 9 gaps that Florence has automatically identified from COOP. The gaps are organized by category using filter buttons: All (9), Recapture (0), Suspect (4), Quality (3), and Frailty (2). The initial view shows the first two gaps; scrolling reveals additional gaps including Type 2 Diabetes with Hyperglycemia, Colorectal Cancer Screening, Statin Therapy, Nephrology Referral, Fall Risk Assessment, Comprehensive Frailty Assessment, and Morbid Obesity.

Care Gaps - Initial View
Figure 3: The initial Care & Diagnosis Gaps view showing the first two of 9 identified gaps: Chronic Kidney Disease Stage 3a (Suspect, COOP, High — ICD-10: N18.31, HCC 138) and Major Depressive Disorder (Suspect, COOP, High — ICD-10: F33.1, HCC 59). Each gap includes a "Stage for MD Review" button and a "Details" expander.
Care Gaps - Scrolled View
Figure 4: Scrolling the gaps list reveals additional gaps, including Type 2 Diabetes with Hyperglycemia (Suspect, ICD-10: E11.65, HCC 19), Colorectal Cancer Screening (Quality), and Statin Therapy for Cardiovascular Disease (Quality). The "Future Visit" button appears on gaps that can be deferred.
Demo Script — Navigator View

Sales Rep: "What you're seeing here is the Florence AI Navigator, integrated directly into athenaOne. The moment Jane Doe was discharged from the hospital, Florence received a notification from COOP and automatically initiated the pre-encounter workflow.

Florence has already compiled all of Jane's relevant information, including her discharge summary, medications, and all of her open care gaps from COOP. You can see here that Florence has identified 9 gaps in total: 4 suspect gaps, 3 quality measures, and 2 frailty indicators. Let me scroll down to show you the full list."

4
Expand Gap Details

Clicking the Details button on any gap reveals comprehensive information including the full ICD-10 description, supporting evidence from the patient's chart, and the clinical rationale for the gap identification. This level of detail is what enables providers to make informed decisions at the point of care.

Gap Details Expanded
Figure 5: Expanded details for the Chronic Kidney Disease Stage 3a suspect gap, showing the full ICD-10 description ("Chronic kidney disease, stage 3a. Stage 3a is defined as eGFR 45–59 mL/min/1.73m²"), supporting evidence from the chart (eGFR: 52 mL/min/1.73m², Creatinine: 1.4 mg/dL), and the "Stage for MD Review" action button.
5
Stage Gaps for Provider Review

Each gap has a "Stage for MD Review" button that the navigator uses to prepare gaps for the provider encounter. The navigator reviews each gap and stages the ones that are relevant for the upcoming visit. Gaps that are not appropriate for the current visit can be deferred using the "Future Visit" button. This targeted staging ensures the provider sees only the most relevant gaps during the encounter.

Stage Buttons
Figure 6: The "Stage for MD Review" buttons visible on each care gap. The navigator can stage individual gaps for the provider encounter or defer them to a future visit. The "Show staged items" checkbox allows the navigator to filter the view to see only staged gaps.
Demo Script — Staging Gaps

Sales Rep: "Now, let's look at how Florence prepares for the provider encounter. The navigator reviews each gap and clicks 'Stage for MD Review' to prepare it for Dr. Campbell. Florence has also automatically generated the necessary MEAT criteria documentation based on the information from COOP. This ensures that the provider has everything they need to close the gaps and that the documentation is compliant."

Chapter 5: The Engage Workflow — AI-Powered Patient Outreach

The Engage workflow is Florence's AI-powered voice outreach tool. It automates a wide range of patient communication tasks, from TCM calls to appointment reminders, using natural language processing to have human-like conversations with patients.

FeatureDescription
Natural Language ConversationEngage uses advanced NLP to have human-like conversations with patients, adapting tone and content based on patient responses.
Automated SchedulingEngage can automatically schedule appointments based on the provider's availability and the patient's preferences.
Real-time EscalationIf a patient expresses confusion, distress, or reports concerning symptoms, Engage automatically escalates the call to a human navigator.
Full TranscriptionEvery call is transcribed in real-time and saved to the patient's record for documentation and compliance purposes.
Important Note on AI Limitations: While Florence's Engage workflow uses advanced AI to conduct patient conversations, it is designed as a support tool, not a replacement for clinical judgment. All AI-generated documentation is reviewed by clinical staff before being finalized. Florence includes built-in escalation protocols to transfer calls to human navigators when clinical judgment is required.
6
Initiate the Engage Workflow

Clicking the Engage button initiates the AI-powered TCM outreach call. The button changes to "In Call..." to indicate the call is in progress, as shown in Figure 7. Florence automatically dials the patient and begins the conversation using the appropriate protocol (in this case, a post-discharge TCM call).

Engage In Call State
Figure 7: The Engage workflow in progress. Note the Engage button now displays "In Call..." status (highlighted in teal), indicating Florence is actively conducting the AI-powered TCM outreach call with the patient. The Convene and Check-In buttons remain available for other workflows.
7
View the Live Transcript

The Transcript tab shows the real-time conversation between Florence and the patient. Florence initiates the call with a warm, professional greeting and confirms the patient's identity before proceeding with the TCM protocol. The full transcript is automatically saved to the patient's record.

Engage Transcript
Figure 8: The Engage transcript showing the real-time conversation between Florence and the patient. The sidebar displays the expanded gap details with the full ICD-10 description and supporting evidence, demonstrating how Florence provides clinical context alongside the patient interaction.

Full Engage Workflow Transcript: Jane Doe TCM Call

Florence: "Hello, I'm calling from Dr. Campbell's office on behalf of Nightingale Health. May I please speak with Jane Doe?"

Jane Doe: "This is she."

Florence: "Hi Jane, my name is Florence, and I'm an AI assistant with Nightingale Health. I'm calling to check in with you after your recent discharge from St. Joseph's Hospital. How are you feeling?"

Jane Doe: "Oh, hello. I'm feeling a little tired, but I'm doing okay."

Florence: "I'm glad to hear that. I'm calling to make sure you have everything you need for a smooth recovery. I see that you were prescribed a few new medications. Have you been able to pick them up from the pharmacy?"

Jane Doe: "Yes, I have them right here."

Florence: "That's great. It's also very important that you have a follow-up appointment with your primary care provider, Dr. Campbell, within the next week. I see one has been scheduled for you on Monday, February 18th at 10:00 AM. Does that time still work for you?"

Jane Doe: "Yes, that's perfect."

Florence: "Wonderful. We look forward to seeing you then. If you have any questions before your appointment, please don't hesitate to call our office. Have a great day, Jane."

Jane Doe: "Thank you, you too."

8
Review Auto-Generated Documentation

After the Engage call completes, the Documentation tab shows the auto-generated clinical documentation. Florence automatically creates a structured summary of the call, including key findings, patient responses, and next steps — all formatted to meet MEAT criteria compliance requirements.

Engage Documentation
Figure 9: The Documentation tab showing Florence's auto-generated clinical documentation from the Engage call, including the call summary, medication verification results, appointment confirmation, and recommended follow-up actions.
Demo Script — Engage Workflow

Sales Rep: "As you can see, Florence had a natural, empathetic conversation with Jane, confirmed her medications, and scheduled her follow-up appointment with Dr. Campbell. This entire process was fully automated, saving the care navigator significant time. The transcript and documentation are automatically saved to Jane's record for compliance purposes."

Part 3: The Encounter Workflow & Provider Perspective

Chapter 6: The Provider's View — A Seamless Encounter

This chapter focuses on the provider's experience during the patient encounter. The key is to demonstrate how Florence acts as an intelligent copilot, streamlining the provider's workflow and enabling them to focus on patient care, not documentation.

Demo Scenario: Jane Doe, 68 y/o female, is in the office for her post-discharge follow-up appointment. Dr. Campbell is seeing Jane for the visit. The navigator has already staged all 9 care gaps for Dr. Campbell to review using the Florence sidebar.
9
Review the Provider View

The Provider View displays all staged gaps with expanded clinical information, including the full ICD-10 description and supporting evidence from the chart. The provider can review each gap, accept or reject it, and Florence automatically generates the MEAT-criteria documentation in real-time.

Provider View
Figure 10: The full dashboard in Provider View, showing the athenaOne EHR on the left with Jane Doe's clinical data (HPI, Vitals, Screenings, Problem List) and the Nightingale Navigator sidebar on the right with staged care gaps and expanded clinical details.
Demo Script — Provider View

Sales Rep: "We're now looking at Dr. Campbell's view in athenaOne. The Florence AI Navigator is seamlessly integrated into the EHR, providing real-time decision support right at the point of care. All the work the navigator did in the pre-encounter phase is now available to Dr. Campbell.

The Florence sidebar on the right displays all 9 of Jane's care gaps that were staged by the navigator. The gaps are organized by type — Suspect, Quality, and Frailty — making it easy for Dr. Campbell to review them. Let's say Dr. Campbell wants to address the Chronic Kidney Disease Stage 3a suspect gap. He can simply click on it to see the supporting evidence from COOP."

10
Review the EHR Patient Chart

The left panel of the dashboard shows the full athenaOne EHR patient chart, including the Subjective section (HPI, ROS), Objective section (Vitals, Screenings & Findings), Problem List, Medications, Referrals, and more. Florence integrates seamlessly alongside this existing workflow without disrupting the provider's familiar EHR experience.

Clinical Data PointValueSignificance
Chief ComplaintPost-hospital discharge follow-up (TCM call)Triggers TCM billing requirements
Blood Pressure142/88 mmHgElevated; supports hypertension gap
BMI29.3Overweight; relevant to obesity gap
PHQ-9 Score8 (Mild)Supports depression screening but may not support moderate severity coding
Morse Fall Scale55 (High Risk)Supports frailty assessment gap
Last A1c8.9% (12/20/2025)Elevated; supports diabetes management gap
eGFR42 mL/minStage 3a CKD; supports nephrology referral

Chapter 7: Provider Best Practices & Objection Handling

Best Practices for Providers

Trust the Staged Gaps: The care gaps staged by Florence are based on real-time data from COOP, which is the source of truth for gap validation. Providers can trust that these gaps are accurate and up-to-date.

Leverage the Sidebar: The Florence sidebar is designed to be an at-a-glance resource. Providers should use it to quickly review all open gaps and supporting evidence without leaving the patient's chart.

Embrace One-Click Attestation: The one-click MEAT criteria attestation is a powerful time-saving feature. Providers should use it to quickly document their clinical decisions rather than manually typing notes.

Review Auto-Generated Documentation: While Florence's documentation is highly accurate, providers should always give it a quick review before signing the note. This ensures clinical accuracy and maintains the provider's professional responsibility.

Common Objections & Responses

"What happens when the AI makes a mistake?"
Response: "Florence is designed as a decision-support tool, not a replacement for clinical judgment. Every AI-generated recommendation includes the supporting evidence from the patient's chart and COOP, so the provider can verify the accuracy before accepting. Additionally, all documentation goes through a review step before being filed to the EHR. Florence also includes built-in escalation protocols — if the AI detects uncertainty or a patient reports concerning symptoms, it automatically transfers to a human navigator."
"How does this integrate with our existing athenaOne workflow?"
Response: "Florence is designed to work within athenaOne, not replace it. The Navigator sidebar appears alongside the patient's chart, so providers never have to leave their familiar EHR environment. Florence uses generative browsing technology — not an API — to interact with athenaOne, which means there's no complex integration required. It works with your existing athenaOne setup."
"Is the patient data secure? What about HIPAA compliance?"
Response: "Absolutely. NightingaleMD is fully HIPAA-compliant. All patient data is encrypted in transit and at rest. Florence operates within the same security perimeter as your existing EHR. We also maintain a BAA (Business Associate Agreement) with all of our clients. The AI models are trained on de-identified data and do not retain patient information after processing."
"What if a patient doesn't want to talk to an AI?"
Response: "Florence always identifies itself as an AI assistant at the beginning of every call. If a patient expresses discomfort or requests to speak with a human, Florence immediately transfers the call to a care navigator. In our pilot program, over 85% of patients completed the full AI-assisted call without requesting a transfer."

Part 4: Post-Encounter & Ongoing Management

Chapter 8: The Convene Workflow — Care Team Collaboration

The Convene workflow facilitates seamless communication and collaboration among the patient's care team. It allows navigators to quickly schedule and launch three-way calls between the patient, the provider, and any other relevant stakeholders (e.g., specialists, family members).

FeatureDescription
Three-Way CallingEasily initiate three-way calls with the patient and other care team members for coordinated care discussions.
Automated SchedulingSchedule calls in advance and send automated reminders to all participants.
Real-time TranscriptionAll calls are transcribed in real-time, and the transcript is automatically saved to the patient's chart.
11
Initiate the Convene Workflow

Clicking the Convene button initiates a three-way call. The button changes to "In Session..." to indicate the call is in progress, as shown in Figure 11. Florence automatically dials the patient first, then connects the care team member.

Convene In Session
Figure 11: The Convene workflow in progress. The Convene button now displays "In Session..." status, indicating Florence is facilitating the three-way call between the patient, care manager, and provider. The Engage button remains available for separate outreach.
Demo Script — Convene Workflow

Sales Rep: "Let's imagine that during Jane's visit, Dr. Campbell decides it would be beneficial to have a follow-up conversation with her cardiologist. With Florence, he doesn't have to waste time playing phone tag. The navigator can simply use the Convene workflow to schedule a three-way call. Florence will automatically dial all participants and connect them. The entire conversation is transcribed in real-time and saved to Jane's chart."

Chapter 9: The Check-In Workflow — Proactive Patient Monitoring

The Check-In workflow is Florence's automated patient monitoring tool. It uses SMS to proactively check in with patients, track their progress, and identify potential issues before they become serious.

FeatureDescription
Automated SMS OutreachSend automated, personalized SMS messages to patients to check on their health status and medication adherence.
Customizable ProtocolsCreate custom check-in protocols for different chronic conditions (e.g., diabetes, hypertension, CHF, post-discharge).
Real-time AlertsIf a patient reports a concerning symptom, Florence automatically alerts the care navigator for immediate follow-up.
12
Initiate the Check-In Workflow

Clicking the Check-In button initiates the SMS-based patient monitoring workflow. Florence sends personalized messages based on the patient's condition and care plan, and monitors responses for any concerning symptoms that may require escalation.

Check-In Workflow
Figure 12: The Check-In workflow showing the SMS-based patient monitoring interface. Florence sends personalized messages to Jane Doe based on her post-discharge care plan, monitoring symptoms, medication adherence, and recovery progress.
Demo Script — Check-In Workflow

Sales Rep: "Florence's support for Jane doesn't end when she leaves the office. Using the Check-In workflow, the care navigator can set up a protocol to monitor Jane's recovery. Florence sends Jane daily SMS messages to ask about her symptoms and medication adherence. If Jane reports any issues — like shortness of breath or dizziness — Florence immediately alerts the care navigator. This proactive approach helps prevent hospital readmissions and improve patient outcomes."

Chapter 10: Filing to the EHR & Conclusion

Closing the Loop

Once the provider has reviewed and accepted the care gaps, Florence makes it easy to file all necessary documentation to the EHR. This is a critical step in closing the loop and ensuring the patient's chart is complete and up-to-date.

FeatureDescription
Generative BrowsingFlorence uses generative browsing (not an API) to securely and reliably file documentation to athenaOne, working within the existing EHR interface.
One-Click FilingWith a single click of the "File to EHR" button, all auto-generated documentation is filed to the patient's chart.
Real-time SyncThe sync between Florence and the EHR is real-time, ensuring the patient's chart is always up-to-date.

The Future of CCA

Florence is more than just a tool; it is a new way of thinking about Comprehensive Care Assessment. By automating the manual, time-consuming tasks that have traditionally burdened care navigators and providers, Florence empowers them to focus on what they do best: providing high-quality, patient-centered care.

70% Navigator Workload Reduction Internal pilot data*
40% Gap Closure Improvement Internal pilot data*
100% Automated Documentation MEAT-criteria compliant*

*Based on NightingaleMD internal pilot program, Q3 2025. Results may vary. Full methodology available upon request.

Appendices

Appendix A: Screenshot Reference Guide

FigureDescriptionKey Elements Shown
1Full Dashboard OverviewathenaOne EHR + Nightingale Navigator sidebar, patient chart, care gaps
2Copilot Sidebar (Zoomed)Patient summary, workflow buttons, Florence Summary, gap filters
3Care & Diagnosis Gaps (Initial)First 2 of 9 gaps: CKD Stage 3a, Major Depressive Disorder
4Care & Diagnosis Gaps (Scrolled)Additional gaps: Type 2 Diabetes, Colorectal Cancer Screening, Statin Therapy
5Gap Details ExpandedFull ICD-10 description, supporting evidence, clinical rationale
6Stage for MD Review ButtonsStaging buttons, "Show staged items" filter, gap categories
7Engage "In Call..." StateEngage button showing active call status
8Engage TranscriptReal-time conversation with expanded gap details
9Engage DocumentationAuto-generated clinical documentation from the call
10Provider View (Full Dashboard)Provider perspective with staged gaps and clinical data
11Convene "In Session..." StateConvene button showing active session status
12Check-In SMS TranscriptSMS-based patient monitoring conversation

Appendix B: Quick Reference — Demo Flow Cheat Sheet

For a streamlined 15–20 minute demo, follow this flow:

StepActionKey Talking PointTime
1Open DashboardShow side-by-side EHR + Copilot layout1 min
2Review Patient SummaryHighlight High Risk, CCM badges, workflow buttons1 min
3Review GapsShow 9 auto-identified gaps from COOP, scroll through list2 min
4Expand a GapShow CKD Stage 3a details with ICD-10 and evidence2 min
5Stage GapsDemonstrate staging for MD review1 min
6Click EngageShow "In Call..." state, demonstrate AI-powered TCM call3 min
7Show TranscriptWalk through the natural conversation2 min
8Show DocumentationHighlight auto-generated clinical notes1 min
9Provider ViewShow provider's perspective with MEAT criteria2 min
10Click ConveneDemonstrate three-way calling2 min
11Click Check-InDemonstrate SMS monitoring1 min
12Wrap UpSummarize KPIs, answer questions2 min

Total Demo Time: 15–20 minutes

Application URL: https://florence-intel-dashboard.vercel.app

HIPAA & Privacy Disclaimer: All patient data displayed in this manual and in the Florence AI Navigator demo application is simulated and does not represent actual patients or protected health information (PHI). NightingaleMD maintains full HIPAA compliance, including encryption of all data in transit and at rest, Business Associate Agreements (BAAs) with all clients, role-based access controls, and comprehensive audit logging. The AI models used by Florence are trained on de-identified data and do not retain patient information after processing.
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